2 min readBengaluruUpdated: Jun 28, 2026 04:09 PM IST
A research paper authored by a team from the Indian Institute of Science (IISC) Bengaluru reached the finals of the CVPR 2026 event in Colorado, USA, held earlier this month. The annual conference on Computer Vision and Pattern Recognition (CVPR) is a major global event that deals with software questions involving the recognition of images by computers and other related matters.
The research paper, Rethinking Dataset Distillation: Hard Truths about Soft Labels, was authored by Priyam Dey, R Venkatesh Babu, Additya Sahdev, Sunny Bhati, and Konda Reddy Mopuri of IISC’s Computational and Data Science (CDS) Department. Out of around 16,000 submissions for the event, this paper came within the top 15, according to an IISC announcement earlier this month.
CDS Head Professor R Venkatesh Babu spoke to The Indian Express on the applications of their research, explaining the concept of “dataset distillation”.
He said, “With AI, we have a large amount of data used in training models… you may need a very large and expensive network of training data. In a dataset where so many samples are available, can we get a handful of samples with which we can train the AI model? Then the training cost can come down drastically.”
Professor Babu explained that token-based AIs like Claude were costly, with expenses like electricity, Graphics Processing Units, and other infrastructure. He added, “This is kind of a deviation from what people were doing continuously – we wanted to look back and say, this is not the correct way. Random (training data) samples also give you the same accuracy.”
Currently, the research was applied to the classification of images – for instance, a situation where a million images would have to be sorted into 1,000 different categories. Babu noted that similar research could also be applied in other domains, such as audio samples.
He explained the application of such research to reducing pollution caused by AI, “The volume of data is so much that we never pay attention to it and feed whatever is available to the machinery… that is what is emitting huge amounts of carbon. Any effort to reduce this amount of data could significantly reduce the carbon footprint.”